Is older age an appropriate criterion alone for ordering cervical spine computed tomography after trauma
Bibliographic record
Abstract
BACKGROUND: Cervical spine computed tomography (CSCT) scans are frequently performed in older emergency department (ED) trauma patients based on the 65-year-old high-risk criterion of the Canadian Cervical Spine Rule (CCR). We sought to determine the positivity rate of CSCT scans in symptomatic and asymptomatic patients to assess the current applicability of age in the CCR. METHODS: We reviewed CSCT ED reports from two institutional hospitals from 2018 to 2023. The primary variable was age; however, we also recorded fracture types and sites and type of treatments. Patients were separated into symptomatic and asymptomatic cohorts. We used a Fisher's exact test to compare variables between the asymptomatic and symptomatic groups and chi-square tests for comparison between age groups. RESULTS: Of 9455 CSCTs performed in patients ≥ 65 years, 192 (2.0%) fractures were identified (113 females); 28 (0.30%) were in asymptomatic patients. The rates of fractures (1.6%) and asymptomatic fractures (0.18%) were lowest in the 65- to 70-year age group. There were no distinguishing features as to the level or part of the vertebra fractured or surgical treatment rate between asymptomatic and symptomatic patients. CONCLUSIONS: Cervical spine fractures in posttrauma patients ≥ 65 years are uncommon, with the lowest incidence in those 65 to 70 years old. Excluding asymptomatic individuals aged 65-70 from routine CSCT presents a minimal risk of missed fractures (0.18%). This prompts consideration for refining age-based screening and integrating shared decision making into the clinical protocol for this demographic, reflecting the low incidence of fractures and the changing health profile of the aging population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".